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Challenge
Challenge
The efficacy of both human tutors and AI tutors (e.g., Intelligent Tutoring Systems and Adaptive Tutoring Systems) is well established. However, human tutoring can be difficult to scale due to human factors, whereas AI tutors lack the accountability structures and implementation guardrails needed to ensure consistent student use, as highlighted by Holt's recent "5% problem" article.
This raises important questions: can combining AI tutor use with human tutor support help mitigate the limitations of each approach? And more importantly, can a human-AI tutoring model produce synergistic benefits beyond what either approach could achieve alone?
Solution
Solution
PLUS is a hybrid human-AI tutoring program in which college tutors provide supplemental motivational and content support to school students as they use AI tutors (e.g., IXL, MATHia, iReady) for math practice. To date, PLUS has provided tutoring support to students across six U.S. states: PA, NY, OR, CA, FL, and WV.
Highlights
Highlights
Human–AI tutoring approximately doubles academic growth (+61% on MAP) while also improving student time on task (+25%) and skill proficiency (+36%) in the AI tutor (IXL), compared to AI-only tutoring (Gurung et al., 2026).
Virtual Human-AI tutoring ($397-$700) is significantly less costly than in-person human tutoring ($3,500-$4,300) and in-person human-AI tutoring ($2,450-$3,000) (Thomas et al., 2023; Gurung et al., 2025; Gurung et al., 2026).
Key Translation Results: From In-Person to Remote Settings
Key Translation Results: From In-Person to Remote Settings
In-person Human-AI tutoring
In-person Human-AI tutoring
Access to in-person supplemental human tutor support during AI tutor practice nearly doubled (2x) students’ academic growth on MAP: treatment students gained 6.8 points, compared with 3.6 points for matched controls (Chine et al., 2022).
Remote Human-AI tutoring
Remote Human-AI tutoring
Access to remote supplemental human tutor support during AI tutor practice increased MAP growth by +0.61 relative to AI-only support, with Human–AI students achieving nearly double (1.9x) expected annual growth within the locally comparable IK bandwidth (Gurung et al., 2026).
On-going Research
On-going Research
As PLUS has matured across four years of field implementation, we are now conducting an RCT to rigorously examine program impact and extend prior formative quasi-experimental evaluations.
We are also conducting simulation studies and implementation evaluations to identify sources of variation in implementation, including variance at the student, teacher, and school context, with implications for how mitigating such variance at scale would further enhance the benefits of human–AI tutoring.
Discoveries
Discoveries
New paradigm for scaling Human-AI tutoring: Remote human-AI tutoring provides empirical support for a scalable alternative that improves student learning by providing supplemental human support during AI tutor practice.
Adaptive human tutor allocation benefits all: Proactively supporting lower-performing students while providing reactive, on-demand support to higher-performing students improves the learning outcomes of the entire cohort (Gurung et al., 2026).
Selected Publication
Selected Publication
Gurung, A., Lin, J., Huang, Z., Borchers, C., Baker, R., Aleven, V., & Koedinger, K. (2025). Starting Seatwork Earlier as a Valid Measure of Student Engagement. In Proceedings of the 18th International Conference on Educational Data Mining (pp. 303-316).
Gurung, A., Gutterman, J., Thomas, D. R., Feng, M., Aleven, V., & Koedinger, K. R. (2026). Coasting Through Class: Learning Opportunity Loss from Practice Avoidance During Individual Seatwork. arXiv preprint arXiv:2604.25014.
Cross-Subject Predictive Validity of Delayed Start Behavior.
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